Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations1058
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory645.7 KiB
Average record size in memory624.9 B

Variable types

Text3
Numeric20
Categorical4

Alerts

52 Weeks High is highly overall correlated with 52 Weeks Low and 6 other fieldsHigh correlation
52 Weeks Low is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Chiffre d'affaires is highly overall correlated with EBITDA and 5 other fieldsHigh correlation
Country is highly overall correlated with CurrencyHigh correlation
Currency is highly overall correlated with CountryHigh correlation
Dividend Per Share Annual is highly overall correlated with 52 Weeks High and 4 other fieldsHigh correlation
EBITDA is highly overall correlated with Chiffre d'affaires and 5 other fieldsHigh correlation
EPS Annual is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Market Cap (in M) is highly overall correlated with 52 Weeks High and 9 other fieldsHigh correlation
Price is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Price 52 Weeks Ago is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
ROI Annual is highly overall correlated with EPS AnnualHigh correlation
Résultat net is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Total assets is highly overall correlated with Chiffre d'affaires and 5 other fieldsHigh correlation
Volume 1 month is highly overall correlated with Chiffre d'affaires and 5 other fieldsHigh correlation
Volume 52 weeks is highly overall correlated with Chiffre d'affaires and 5 other fieldsHigh correlation
Country is highly imbalanced (83.4%)Imbalance
Currency is highly imbalanced (94.6%)Imbalance
Volume 52 weeks is highly skewed (γ1 = 28.74766139)Skewed
Volume 1 month is highly skewed (γ1 = 24.66839365)Skewed
Symbol has unique valuesUnique
Company Name has unique valuesUnique
Market Cap (in M) has unique valuesUnique
Beta has unique valuesUnique
Volume 52 weeks has unique valuesUnique
Performance (52 weeks) has unique valuesUnique
Price 52 Weeks Ago has unique valuesUnique
Dividend Per Share Annual has 14 (1.3%) zerosZeros
Ratio Debt/Equity (Annual) has 95 (9.0%) zerosZeros
Dividend Yield Indicated Annual has 16 (1.5%) zerosZeros

Reproduction

Analysis started2024-08-13 14:51:57.590201
Analysis finished2024-08-13 14:52:39.417853
Duration41.83 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Symbol
Text

UNIQUE 

Distinct1058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
2024-08-13T16:52:39.679634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2844991
Min length1

Characters and Unicode

Total characters3475
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1058 ?
Unique (%)100.0%

Sample

1st rowCOLM
2nd rowHCKT
3rd rowLFUS
4th rowBRKR
5th rowVALU
ValueCountFrequency (%)
colm 1
 
0.1%
wmg 1
 
0.1%
chx 1
 
0.1%
nwsa 1
 
0.1%
lfus 1
 
0.1%
brkr 1
 
0.1%
valu 1
 
0.1%
artna 1
 
0.1%
sga 1
 
0.1%
crct 1
 
0.1%
Other values (1048) 1048
99.1%
2024-08-13T16:52:40.120317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 273
 
7.9%
A 239
 
6.9%
S 236
 
6.8%
R 219
 
6.3%
T 210
 
6.0%
N 189
 
5.4%
L 184
 
5.3%
E 184
 
5.3%
I 175
 
5.0%
M 174
 
5.0%
Other values (16) 1392
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 273
 
7.9%
A 239
 
6.9%
S 236
 
6.8%
R 219
 
6.3%
T 210
 
6.0%
N 189
 
5.4%
L 184
 
5.3%
E 184
 
5.3%
I 175
 
5.0%
M 174
 
5.0%
Other values (16) 1392
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 273
 
7.9%
A 239
 
6.9%
S 236
 
6.8%
R 219
 
6.3%
T 210
 
6.0%
N 189
 
5.4%
L 184
 
5.3%
E 184
 
5.3%
I 175
 
5.0%
M 174
 
5.0%
Other values (16) 1392
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 273
 
7.9%
A 239
 
6.9%
S 236
 
6.8%
R 219
 
6.3%
T 210
 
6.0%
N 189
 
5.4%
L 184
 
5.3%
E 184
 
5.3%
I 175
 
5.0%
M 174
 
5.0%
Other values (16) 1392
40.1%

Company Name
Text

UNIQUE 

Distinct1058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.7 KiB
2024-08-13T16:52:40.350996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length44
Median length33
Mean length19.058601
Min length5

Characters and Unicode

Total characters20164
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1058 ?
Unique (%)100.0%

Sample

1st rowColumbia Sportswear Co
2nd rowHackett Group Inc
3rd rowLittelfuse Inc
4th rowBruker Corp
5th rowValue Line Inc
ValueCountFrequency (%)
inc 592
 
18.6%
corp 241
 
7.6%
co 85
 
2.7%
group 67
 
2.1%
holdings 52
 
1.6%
ltd 41
 
1.3%
energy 41
 
1.3%
international 38
 
1.2%
31
 
1.0%
lp 26
 
0.8%
Other values (1327) 1966
61.8%
2024-08-13T16:52:40.711239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2122
 
10.5%
n 1730
 
8.6%
e 1414
 
7.0%
r 1414
 
7.0%
o 1380
 
6.8%
a 1078
 
5.3%
c 1013
 
5.0%
i 967
 
4.8%
t 952
 
4.7%
s 906
 
4.5%
Other values (56) 7188
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2122
 
10.5%
n 1730
 
8.6%
e 1414
 
7.0%
r 1414
 
7.0%
o 1380
 
6.8%
a 1078
 
5.3%
c 1013
 
5.0%
i 967
 
4.8%
t 952
 
4.7%
s 906
 
4.5%
Other values (56) 7188
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2122
 
10.5%
n 1730
 
8.6%
e 1414
 
7.0%
r 1414
 
7.0%
o 1380
 
6.8%
a 1078
 
5.3%
c 1013
 
5.0%
i 967
 
4.8%
t 952
 
4.7%
s 906
 
4.5%
Other values (56) 7188
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2122
 
10.5%
n 1730
 
8.6%
e 1414
 
7.0%
r 1414
 
7.0%
o 1380
 
6.8%
a 1078
 
5.3%
c 1013
 
5.0%
i 967
 
4.8%
t 952
 
4.7%
s 906
 
4.5%
Other values (56) 7188
35.6%

Price
Real number (ℝ)

HIGH CORRELATION 

Distinct1024
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.93126
Minimum0.65
Maximum1259.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:40.837075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.65
5-th percentile9.948
Q127.6275
median60.34
Q3122.755
95-th percentile343.1155
Maximum1259.41
Range1258.76
Interquartile range (IQR)95.1275

Descriptive statistics

Standard deviation136.63641
Coefficient of variation (CV)1.3146805
Kurtosis17.981578
Mean103.93126
Median Absolute Deviation (MAD)39.28
Skewness3.5918526
Sum109959.28
Variance18669.507
MonotonicityNot monotonic
2024-08-13T16:52:41.072933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.05 2
 
0.2%
39.3 2
 
0.2%
27.83 2
 
0.2%
72.85 2
 
0.2%
11.45 2
 
0.2%
39.51 2
 
0.2%
27.25 2
 
0.2%
44.92 2
 
0.2%
40.65 2
 
0.2%
10.31 2
 
0.2%
Other values (1014) 1038
98.1%
ValueCountFrequency (%)
0.65 1
0.1%
0.82 1
0.1%
1.88 1
0.1%
2.21 1
0.1%
2.28 1
0.1%
2.48 1
0.1%
2.7 1
0.1%
2.79 1
0.1%
2.94 1
0.1%
3.14 1
0.1%
ValueCountFrequency (%)
1259.41 1
0.1%
1246.1 1
0.1%
979.31 1
0.1%
891.68 1
0.1%
856.21 1
0.1%
854.93 1
0.1%
823 1
0.1%
822.57 1
0.1%
818.88 1
0.1%
807.9 1
0.1%

Market Cap (in M)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34480.4
Minimum9.4014712
Maximum3287742.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:41.190300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.4014712
5-th percentile302.87645
Q11572.3152
median5665.3639
Q320049.286
95-th percentile124954.87
Maximum3287742.5
Range3287733.1
Interquartile range (IQR)18476.97

Descriptive statistics

Standard deviation170059.19
Coefficient of variation (CV)4.9320538
Kurtosis263.55744
Mean34480.4
Median Absolute Deviation (MAD)5041.3008
Skewness15.268515
Sum36480264
Variance2.8920128 × 1010
MonotonicityNot monotonic
2024-08-13T16:52:41.308453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4781.357487 1
 
0.1%
2078.176537 1
 
0.1%
39783.1898 1
 
0.1%
8835.765375 1
 
0.1%
6448.512956 1
 
0.1%
40287.8928 1
 
0.1%
1761.716628 1
 
0.1%
7898.100782 1
 
0.1%
14734.37778 1
 
0.1%
5313.711808 1
 
0.1%
Other values (1048) 1048
99.1%
ValueCountFrequency (%)
9.401471162 1
0.1%
16.7990919 1
0.1%
21.00516654 1
0.1%
36.33234423 1
0.1%
37.94747326 1
0.1%
50.30779184 1
0.1%
50.72976707 1
0.1%
55.736357 1
0.1%
64.32330877 1
0.1%
79.46458337 1
0.1%
ValueCountFrequency (%)
3287742.486 1
0.1%
3017962.34 1
0.1%
2581647.096 1
0.1%
847457.4806 1
0.1%
690133.0242 1
0.1%
546558.8075 1
0.1%
528029.9458 1
0.1%
514274.3533 1
0.1%
513261.5504 1
0.1%
421989.328 1
0.1%

P/E Ratio
Real number (ℝ)

Distinct1055
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.480152
Minimum0.1795
Maximum1238.3441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:41.437783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1795
5-th percentile5.400815
Q112.313775
median20.7235
Q332.767625
95-th percentile74.42456
Maximum1238.3441
Range1238.1646
Interquartile range (IQR)20.45385

Descriptive statistics

Standard deviation57.254848
Coefficient of variation (CV)1.8784305
Kurtosis237.65297
Mean30.480152
Median Absolute Deviation (MAD)9.6313
Skewness13.38652
Sum32248
Variance3278.1176
MonotonicityNot monotonic
2024-08-13T16:52:41.569612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.4093 2
 
0.2%
10.3985 2
 
0.2%
15.2663 2
 
0.2%
111.8723 1
 
0.1%
19.4876 1
 
0.1%
11.5108 1
 
0.1%
40.645 1
 
0.1%
9.7942 1
 
0.1%
0.4944 1
 
0.1%
18.9494 1
 
0.1%
Other values (1045) 1045
98.8%
ValueCountFrequency (%)
0.1795 1
0.1%
0.2105 1
0.1%
0.4939 1
0.1%
0.4944 1
0.1%
0.6105 1
0.1%
0.7377 1
0.1%
0.9266 1
0.1%
1.3409 1
0.1%
1.4749 1
0.1%
1.4997 1
0.1%
ValueCountFrequency (%)
1238.3441 1
0.1%
795.5134 1
0.1%
715.5796 1
0.1%
358.1528 1
0.1%
280.3127 1
0.1%
256.4198 1
0.1%
235.4212 1
0.1%
209.8124 1
0.1%
207.352 1
0.1%
207.2858 1
0.1%

Beta
Real number (ℝ)

UNIQUE 

Distinct1058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74286485
Minimum-1.0634917
Maximum3.0333507
Zeros0
Zeros (%)0.0%
Negative71
Negative (%)6.7%
Memory size8.4 KiB
2024-08-13T16:52:41.698407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.0634917
5-th percentile-0.06391838
Q10.35112782
median0.71193227
Q31.0582572
95-th percentile1.6718961
Maximum3.0333507
Range4.0968424
Interquartile range (IQR)0.70712941

Descriptive statistics

Standard deviation0.55349461
Coefficient of variation (CV)0.74508117
Kurtosis1.0485764
Mean0.74286485
Median Absolute Deviation (MAD)0.35559463
Skewness0.51699206
Sum785.95102
Variance0.30635629
MonotonicityNot monotonic
2024-08-13T16:52:41.817038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6283727 1
 
0.1%
1.1955191 1
 
0.1%
0.34852982 1
 
0.1%
1.0176492 1
 
0.1%
0.44708386 1
 
0.1%
0.6306574 1
 
0.1%
-0.60414195 1
 
0.1%
0.049116045 1
 
0.1%
1.0020108 1
 
0.1%
0.7603355 1
 
0.1%
Other values (1048) 1048
99.1%
ValueCountFrequency (%)
-1.0634917 1
0.1%
-0.85726035 1
0.1%
-0.8281848 1
0.1%
-0.64495367 1
0.1%
-0.60414195 1
0.1%
-0.57747185 1
0.1%
-0.5686319 1
0.1%
-0.55975103 1
0.1%
-0.53165877 1
0.1%
-0.5231576 1
0.1%
ValueCountFrequency (%)
3.0333507 1
0.1%
2.8911397 1
0.1%
2.8462048 1
0.1%
2.844611 1
0.1%
2.8428986 1
0.1%
2.718199 1
0.1%
2.5481517 1
0.1%
2.4423316 1
0.1%
2.401312 1
0.1%
2.3790329 1
0.1%

Volume 52 weeks
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct1058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2284999.1
Minimum2340.0794
Maximum4.6049593 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:41.936998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2340.0794
5-th percentile37747.956
Q1246951.49
median701750.79
Q31787068.1
95-th percentile6761964.4
Maximum4.6049593 × 108
Range4.6049359 × 108
Interquartile range (IQR)1540116.6

Descriptive statistics

Standard deviation14722913
Coefficient of variation (CV)6.4432906
Kurtosis890.1747
Mean2284999.1
Median Absolute Deviation (MAD)567695.04
Skewness28.747661
Sum2.417529 × 109
Variance2.1676417 × 1014
MonotonicityNot monotonic
2024-08-13T16:52:42.064257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
461903.913 1
 
0.1%
64429.36508 1
 
0.1%
2834971.032 1
 
0.1%
288333.3333 1
 
0.1%
449670.6349 1
 
0.1%
1424572.222 1
 
0.1%
2139752.381 1
 
0.1%
1422067.46 1
 
0.1%
615191.6667 1
 
0.1%
1677946.825 1
 
0.1%
Other values (1048) 1048
99.1%
ValueCountFrequency (%)
2340.079365 1
0.1%
3946.102767 1
0.1%
3981.746032 1
0.1%
6097.619048 1
0.1%
7013.492063 1
0.1%
7264.285714 1
0.1%
7617.384921 1
0.1%
9100.793651 1
0.1%
9144.444444 1
0.1%
10988.09524 1
0.1%
ValueCountFrequency (%)
460495926.5 1
0.1%
60546926.98 1
0.1%
53449109.52 1
0.1%
46498527.38 1
0.1%
37496881.35 1
0.1%
36573550 1
0.1%
30774446.43 1
0.1%
22198478.57 1
0.1%
21313018.65 1
0.1%
19931671.43 1
0.1%

Volume 1 month
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1056
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2305675.7
Minimum2156.5217
Maximum3.4773365 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:42.180982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2156.5217
5-th percentile34857.874
Q1269942.39
median784560.87
Q31844027.2
95-th percentile7276085.7
Maximum3.4773365 × 108
Range3.4773149 × 108
Interquartile range (IQR)1574084.8

Descriptive statistics

Standard deviation11778544
Coefficient of variation (CV)5.1085001
Kurtosis705.16099
Mean2305675.7
Median Absolute Deviation (MAD)619078.26
Skewness24.668394
Sum2.4394048 × 109
Variance1.3873411 × 1014
MonotonicityNot monotonic
2024-08-13T16:52:42.298294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32643.47826 2
 
0.2%
689947.8261 2
 
0.2%
556895.4167 1
 
0.1%
1494300 1
 
0.1%
2679034.783 1
 
0.1%
2339982.609 1
 
0.1%
6316626.087 1
 
0.1%
3077447.826 1
 
0.1%
3871134.783 1
 
0.1%
210186.9565 1
 
0.1%
Other values (1046) 1046
98.9%
ValueCountFrequency (%)
2156.521739 1
0.1%
2600 1
0.1%
6621.73913 1
0.1%
8299.173913 1
0.1%
9004.347826 1
0.1%
9956.521739 1
0.1%
10802.66667 1
0.1%
11278.26087 1
0.1%
12178.26087 1
0.1%
12869.13043 1
0.1%
ValueCountFrequency (%)
347733646.8 1
0.1%
81539721.74 1
0.1%
78406543.48 1
0.1%
58253669.57 1
0.1%
38154608.7 1
0.1%
37939434.78 1
0.1%
31483673.91 1
0.1%
28438804.35 1
0.1%
23389834.78 1
0.1%
22535586.96 1
0.1%

52 Weeks High
Real number (ℝ)

HIGH CORRELATION 

Distinct1036
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.22866
Minimum1.33
Maximum1369.575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:42.423410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.33
5-th percentile13.06275
Q134.03375
median72.595
Q3145.47
95-th percentile388.7915
Maximum1369.575
Range1368.245
Interquartile range (IQR)111.43625

Descriptive statistics

Standard deviation151.36661
Coefficient of variation (CV)1.2589895
Kurtosis16.573525
Mean120.22866
Median Absolute Deviation (MAD)46.145
Skewness3.4570313
Sum127201.92
Variance22911.852
MonotonicityNot monotonic
2024-08-13T16:52:42.538496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.2 2
 
0.2%
123.73 2
 
0.2%
88 2
 
0.2%
43.42 2
 
0.2%
35.24 2
 
0.2%
11.85 2
 
0.2%
55.47 2
 
0.2%
73.97 2
 
0.2%
25.51 2
 
0.2%
35 2
 
0.2%
Other values (1026) 1038
98.1%
ValueCountFrequency (%)
1.33 1
0.1%
1.42 1
0.1%
2.28 1
0.1%
2.85 1
0.1%
3.6 1
0.1%
3.75 1
0.1%
4.02 1
0.1%
4.5 1
0.1%
4.7 1
0.1%
4.83 1
0.1%
ValueCountFrequency (%)
1369.575 1
0.1%
1305.78 1
0.1%
1130 1
0.1%
1033.42 1
0.1%
966 1
0.1%
914.93 1
0.1%
896.42 1
0.1%
896.32 1
0.1%
891.39 1
0.1%
884.62 1
0.1%

52 Weeks Low
Real number (ℝ)

HIGH CORRELATION 

Distinct1034
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.223203
Minimum0.5001
Maximum802.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:42.801227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5001
5-th percentile7.32225
Q120.8525
median45.555
Q394.1375
95-th percentile249.4665
Maximum802.46
Range801.9599
Interquartile range (IQR)73.285

Descriptive statistics

Standard deviation94.936749
Coefficient of variation (CV)1.2293811
Kurtosis12.100612
Mean77.223203
Median Absolute Deviation (MAD)29.6175
Skewness3.019221
Sum81702.148
Variance9012.9864
MonotonicityNot monotonic
2024-08-13T16:52:42.913710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.64 3
 
0.3%
86.1 2
 
0.2%
49.55 2
 
0.2%
184.02 2
 
0.2%
66.47 2
 
0.2%
10.08 2
 
0.2%
37.61 2
 
0.2%
13.68 2
 
0.2%
44.27 2
 
0.2%
36.12 2
 
0.2%
Other values (1024) 1037
98.0%
ValueCountFrequency (%)
0.5001 1
0.1%
0.682 1
0.1%
1.7001 1
0.1%
1.99 1
0.1%
2 1
0.1%
2.15 1
0.1%
2.355 1
0.1%
2.455 1
0.1%
2.56 1
0.1%
2.57 1
0.1%
ValueCountFrequency (%)
802.46 1
0.1%
677.8 1
0.1%
674.58 1
0.1%
614.22 1
0.1%
596.8 1
0.1%
576 1
0.1%
564.19 1
0.1%
530.6 1
0.1%
516.71 1
0.1%
492.84 1
0.1%

Exchange
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
NYSE
733 
NASDAQ
325 

Length

Max length6
Median length4
Mean length4.6143667
Min length4

Characters and Unicode

Total characters4882
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASDAQ
2nd rowNASDAQ
3rd rowNASDAQ
4th rowNASDAQ
5th rowNASDAQ

Common Values

ValueCountFrequency (%)
NYSE 733
69.3%
NASDAQ 325
30.7%

Length

2024-08-13T16:52:43.035979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T16:52:43.145421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nyse 733
69.3%
nasdaq 325
30.7%

Most occurring characters

ValueCountFrequency (%)
N 1058
21.7%
S 1058
21.7%
Y 733
15.0%
E 733
15.0%
A 650
13.3%
D 325
 
6.7%
Q 325
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1058
21.7%
S 1058
21.7%
Y 733
15.0%
E 733
15.0%
A 650
13.3%
D 325
 
6.7%
Q 325
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1058
21.7%
S 1058
21.7%
Y 733
15.0%
E 733
15.0%
A 650
13.3%
D 325
 
6.7%
Q 325
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1058
21.7%
S 1058
21.7%
Y 733
15.0%
E 733
15.0%
A 650
13.3%
D 325
 
6.7%
Q 325
 
6.7%

Performance (52 weeks)
Real number (ℝ)

UNIQUE 

Distinct1058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13021272
Minimum-0.84149176
Maximum2.2818788
Zeros0
Zeros (%)0.0%
Negative353
Negative (%)33.4%
Memory size8.4 KiB
2024-08-13T16:52:43.237936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.84149176
5-th percentile-0.30235049
Q1-0.05702445
median0.10235583
Q30.27094073
95-th percentile0.6394971
Maximum2.2818788
Range3.1233705
Interquartile range (IQR)0.32796518

Descriptive statistics

Standard deviation0.31075619
Coefficient of variation (CV)2.3865272
Kurtosis5.2089657
Mean0.13021272
Median Absolute Deviation (MAD)0.16297981
Skewness1.2178411
Sum137.76505
Variance0.096569409
MonotonicityNot monotonic
2024-08-13T16:52:43.348618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.09814821387 1
 
0.1%
-0.009354984267 1
 
0.1%
0.3613704041 1
 
0.1%
0.04611390849 1
 
0.1%
-0.109469336 1
 
0.1%
0.2659701144 1
 
0.1%
0.2564951627 1
 
0.1%
0.1948445935 1
 
0.1%
0.1200835372 1
 
0.1%
0.09338609219 1
 
0.1%
Other values (1048) 1048
99.1%
ValueCountFrequency (%)
-0.8414917649 1
0.1%
-0.7886233329 1
0.1%
-0.6763890797 1
0.1%
-0.663740615 1
0.1%
-0.6554187887 1
0.1%
-0.6177756213 1
0.1%
-0.6051135051 1
0.1%
-0.5811242052 1
0.1%
-0.5602802047 1
0.1%
-0.53585032 1
0.1%
ValueCountFrequency (%)
2.281878765 1
0.1%
2.054337715 1
0.1%
1.663343856 1
0.1%
1.483330752 1
0.1%
1.478481892 1
0.1%
1.457937319 1
0.1%
1.401461095 1
0.1%
1.319225987 1
0.1%
1.203234182 1
0.1%
1.170286229 1
0.1%

Country
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
US
965 
GB
 
13
BM
 
10
IE
 
10
CN
 
9
Other values (22)
 
51

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2116
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.2%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 965
91.2%
GB 13
 
1.2%
BM 10
 
0.9%
IE 10
 
0.9%
CN 9
 
0.9%
IL 7
 
0.7%
GR 6
 
0.6%
CA 5
 
0.5%
SG 4
 
0.4%
MC 4
 
0.4%
Other values (17) 25
 
2.4%

Length

2024-08-13T16:52:43.454862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 965
91.2%
gb 13
 
1.2%
bm 10
 
0.9%
ie 10
 
0.9%
cn 9
 
0.9%
il 7
 
0.7%
gr 6
 
0.6%
ca 5
 
0.5%
sg 4
 
0.4%
mc 4
 
0.4%
Other values (17) 25
 
2.4%

Most occurring characters

ValueCountFrequency (%)
S 970
45.8%
U 969
45.8%
C 25
 
1.2%
B 24
 
1.1%
G 23
 
1.1%
I 18
 
0.9%
M 16
 
0.8%
E 11
 
0.5%
N 11
 
0.5%
L 11
 
0.5%
Other values (10) 38
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 970
45.8%
U 969
45.8%
C 25
 
1.2%
B 24
 
1.1%
G 23
 
1.1%
I 18
 
0.9%
M 16
 
0.8%
E 11
 
0.5%
N 11
 
0.5%
L 11
 
0.5%
Other values (10) 38
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 970
45.8%
U 969
45.8%
C 25
 
1.2%
B 24
 
1.1%
G 23
 
1.1%
I 18
 
0.9%
M 16
 
0.8%
E 11
 
0.5%
N 11
 
0.5%
L 11
 
0.5%
Other values (10) 38
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 970
45.8%
U 969
45.8%
C 25
 
1.2%
B 24
 
1.1%
G 23
 
1.1%
I 18
 
0.9%
M 16
 
0.8%
E 11
 
0.5%
N 11
 
0.5%
L 11
 
0.5%
Other values (10) 38
 
1.8%

Chiffre d'affaires
Real number (ℝ)

HIGH CORRELATION 

Distinct1055
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5194883 × 1010
Minimum4985000
Maximum9.41168 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:43.563433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4985000
5-th percentile1.7034965 × 108
Q11.002305 × 109
median3.3957839 × 109
Q31.1036582 × 1010
95-th percentile5.8158602 × 1010
Maximum9.41168 × 1011
Range9.4116302 × 1011
Interquartile range (IQR)1.0034277 × 1010

Descriptive statistics

Standard deviation5.0147842 × 1010
Coefficient of variation (CV)3.3003111
Kurtosis147.06217
Mean1.5194883 × 1010
Median Absolute Deviation (MAD)2.9139774 × 109
Skewness10.289546
Sum1.6076187 × 1013
Variance2.5148061 × 1021
MonotonicityNot monotonic
2024-08-13T16:52:43.678770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8899000320 2
 
0.2%
6591000064 2
 
0.2%
7093000192 2
 
0.2%
3385903104 1
 
0.1%
7335277056 1
 
0.1%
5.808000205 × 10101
 
0.1%
2.356799898 × 10101
 
0.1%
1.5568 × 10101
 
0.1%
3140758016 1
 
0.1%
827368000 1
 
0.1%
Other values (1045) 1045
98.8%
ValueCountFrequency (%)
4985000 1
0.1%
11569774 1
0.1%
12186803 1
0.1%
18304184 1
0.1%
20074548 1
0.1%
21890000 1
0.1%
25043000 1
0.1%
27756008 1
0.1%
29360220 1
0.1%
36133000 1
0.1%
ValueCountFrequency (%)
9.41168001 × 10111
0.1%
6.573319782 × 10111
0.1%
3.856030106 × 10111
0.1%
3.854390067 × 10111
0.1%
3.618549924 × 10111
0.1%
3.451349893 × 10111
0.1%
3.137510113 × 10111
0.1%
2.838308127 × 10111
0.1%
2.536950006 × 10111
0.1%
2.45122007 × 10111
0.1%

Résultat net
Real number (ℝ)

HIGH CORRELATION 

Distinct1049
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4174652 × 109
Minimum-6.5410002 × 109
Maximum1.01956 × 1011
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)4.7%
Memory size8.4 KiB
2024-08-13T16:52:43.791227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.5410002 × 109
5-th percentile1523400
Q173342750
median2.9537 × 108
Q39.7575 × 108
95-th percentile5.6974499 × 109
Maximum1.01956 × 1011
Range1.08497 × 1011
Interquartile range (IQR)9.0240725 × 108

Descriptive statistics

Standard deviation5.5247326 × 109
Coefficient of variation (CV)3.8976144
Kurtosis202.27891
Mean1.4174652 × 109
Median Absolute Deviation (MAD)2.675655 × 108
Skewness12.929365
Sum1.4996781 × 1012
Variance3.052267 × 1019
MonotonicityNot monotonic
2024-08-13T16:52:43.905368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1052000000 2
 
0.2%
2560000000 2
 
0.2%
576499968 2
 
0.2%
1190000000 2
 
0.2%
4487000064 2
 
0.2%
1560000000 2
 
0.2%
432000000 2
 
0.2%
975000000 2
 
0.2%
276000000 2
 
0.2%
212440992 1
 
0.1%
Other values (1039) 1039
98.2%
ValueCountFrequency (%)
-6541000192 1
0.1%
-2592999936 1
0.1%
-1163454976 1
0.1%
-711356032 1
0.1%
-691441984 1
0.1%
-655574016 1
0.1%
-646499968 1
0.1%
-554169024 1
0.1%
-313273984 1
0.1%
-248976000 1
0.1%
ValueCountFrequency (%)
1.019560018 × 10111
0.1%
8.813599949 × 10101
0.1%
7.974100173 × 10101
0.1%
4.259799859 × 10101
0.1%
3.416000102 × 10101
0.1%
1.89419991 × 10101
0.1%
1.88180009 × 10101
0.1%
1.872 × 10101
0.1%
1.638200013 × 10101
0.1%
1.509100032 × 10101
0.1%

Sector
Categorical

Distinct11
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
Industrials
226 
Consumer Cyclical
156 
Real Estate
106 
Technology
96 
Financial Services
93 
Other values (6)
381 

Length

Max length22
Median length17
Mean length12.842155
Min length6

Characters and Unicode

Total characters13587
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer Cyclical
2nd rowTechnology
3rd rowTechnology
4th rowHealthcare
5th rowFinancial Services

Common Values

ValueCountFrequency (%)
Industrials 226
21.4%
Consumer Cyclical 156
14.7%
Real Estate 106
10.0%
Technology 96
9.1%
Financial Services 93
8.8%
Energy 90
 
8.5%
Basic Materials 73
 
6.9%
Consumer Defensive 67
 
6.3%
Utilities 62
 
5.9%
Healthcare 59
 
5.6%

Length

2024-08-13T16:52:44.015272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
industrials 226
14.3%
consumer 223
14.1%
cyclical 156
9.9%
services 123
7.8%
real 106
 
6.7%
estate 106
 
6.7%
technology 96
 
6.1%
financial 93
 
5.9%
energy 90
 
5.7%
basic 73
 
4.6%
Other values (5) 291
18.4%

Most occurring characters

ValueCountFrequency (%)
e 1321
 
9.7%
s 1179
 
8.7%
i 1150
 
8.5%
a 1147
 
8.4%
l 1027
 
7.6%
n 948
 
7.0%
r 794
 
5.8%
c 786
 
5.8%
t 724
 
5.3%
525
 
3.9%
Other values (21) 3986
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1321
 
9.7%
s 1179
 
8.7%
i 1150
 
8.5%
a 1147
 
8.4%
l 1027
 
7.6%
n 948
 
7.0%
r 794
 
5.8%
c 786
 
5.8%
t 724
 
5.3%
525
 
3.9%
Other values (21) 3986
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1321
 
9.7%
s 1179
 
8.7%
i 1150
 
8.5%
a 1147
 
8.4%
l 1027
 
7.6%
n 948
 
7.0%
r 794
 
5.8%
c 786
 
5.8%
t 724
 
5.3%
525
 
3.9%
Other values (21) 3986
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1321
 
9.7%
s 1179
 
8.7%
i 1150
 
8.5%
a 1147
 
8.4%
l 1027
 
7.6%
n 948
 
7.0%
r 794
 
5.8%
c 786
 
5.8%
t 724
 
5.3%
525
 
3.9%
Other values (21) 3986
29.3%
Distinct134
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size80.7 KiB
2024-08-13T16:52:44.199915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length30
Mean length21.026465
Min length4

Characters and Unicode

Total characters22246
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)1.3%

Sample

1st rowApparel Manufacturing
2nd rowInformation Technology Services
3rd rowElectronic Components
4th rowMedical Devices
5th rowFinancial Data & Stock Exchanges
ValueCountFrequency (%)
619
 
19.9%
specialty 123
 
3.9%
services 113
 
3.6%
reit 102
 
3.3%
gas 97
 
3.1%
oil 85
 
2.7%
industrial 65
 
2.1%
utilities 62
 
2.0%
equipment 54
 
1.7%
retail 53
 
1.7%
Other values (177) 1745
56.0%
2024-08-13T16:52:44.533928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2093
 
9.4%
2060
 
9.3%
i 1833
 
8.2%
t 1501
 
6.7%
a 1470
 
6.6%
s 1281
 
5.8%
n 1257
 
5.7%
r 1180
 
5.3%
c 1007
 
4.5%
l 994
 
4.5%
Other values (38) 7570
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22246
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2093
 
9.4%
2060
 
9.3%
i 1833
 
8.2%
t 1501
 
6.7%
a 1470
 
6.6%
s 1281
 
5.8%
n 1257
 
5.7%
r 1180
 
5.3%
c 1007
 
4.5%
l 994
 
4.5%
Other values (38) 7570
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22246
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2093
 
9.4%
2060
 
9.3%
i 1833
 
8.2%
t 1501
 
6.7%
a 1470
 
6.6%
s 1281
 
5.8%
n 1257
 
5.7%
r 1180
 
5.3%
c 1007
 
4.5%
l 994
 
4.5%
Other values (38) 7570
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22246
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2093
 
9.4%
2060
 
9.3%
i 1833
 
8.2%
t 1501
 
6.7%
a 1470
 
6.6%
s 1281
 
5.8%
n 1257
 
5.7%
r 1180
 
5.3%
c 1007
 
4.5%
l 994
 
4.5%
Other values (38) 7570
34.0%

Price 52 Weeks Ago
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.030102
Minimum0.81600487
Maximum842.3111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:44.772573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.81600487
5-th percentile9.1900957
Q126.174016
median55.296988
Q3111.24158
95-th percentile284.63046
Maximum842.3111
Range841.49509
Interquartile range (IQR)85.067562

Descriptive statistics

Standard deviation108.34425
Coefficient of variation (CV)1.1902025
Kurtosis10.684278
Mean91.030102
Median Absolute Deviation (MAD)35.641743
Skewness2.8831609
Sum96309.848
Variance11738.477
MonotonicityNot monotonic
2024-08-13T16:52:44.882124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.5400238 1
 
0.1%
173.9427948 1
 
0.1%
58.71107864 1
 
0.1%
173.6832275 1
 
0.1%
75.86323547 1
 
0.1%
232.3593597 1
 
0.1%
8.696279526 1
 
0.1%
32.92409897 1
 
0.1%
102.1669769 1
 
0.1%
22.31230736 1
 
0.1%
Other values (1048) 1048
99.1%
ValueCountFrequency (%)
0.8160048723 1
0.1%
0.9495311975 1
0.1%
1.714285612 1
0.1%
2.333800077 1
0.1%
2.626829386 1
0.1%
2.813369274 1
0.1%
3.216960907 1
0.1%
3.351722002 1
0.1%
3.584943533 1
0.1%
3.70450449 1
0.1%
ValueCountFrequency (%)
842.3110962 1
0.1%
758.5200806 1
0.1%
726.2716675 1
0.1%
708.7266235 1
0.1%
677.3247681 1
0.1%
666.3399658 1
0.1%
645.6385498 1
0.1%
614.9504395 1
0.1%
587.0484619 1
0.1%
545.9385376 1
0.1%

Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
USD
1040 
CNY
 
9
CAD
 
3
EUR
 
2
BRL
 
1
Other values (3)
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3174
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 1040
98.3%
CNY 9
 
0.9%
CAD 3
 
0.3%
EUR 2
 
0.2%
BRL 1
 
0.1%
ZAR 1
 
0.1%
ILS 1
 
0.1%
MXN 1
 
0.1%

Length

2024-08-13T16:52:44.986782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T16:52:45.095837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 1040
98.3%
cny 9
 
0.9%
cad 3
 
0.3%
eur 2
 
0.2%
brl 1
 
0.1%
zar 1
 
0.1%
ils 1
 
0.1%
mxn 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 1043
32.9%
U 1042
32.8%
S 1041
32.8%
C 12
 
0.4%
N 10
 
0.3%
Y 9
 
0.3%
A 4
 
0.1%
R 4
 
0.1%
E 2
 
0.1%
L 2
 
0.1%
Other values (5) 5
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1043
32.9%
U 1042
32.8%
S 1041
32.8%
C 12
 
0.4%
N 10
 
0.3%
Y 9
 
0.3%
A 4
 
0.1%
R 4
 
0.1%
E 2
 
0.1%
L 2
 
0.1%
Other values (5) 5
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1043
32.9%
U 1042
32.8%
S 1041
32.8%
C 12
 
0.4%
N 10
 
0.3%
Y 9
 
0.3%
A 4
 
0.1%
R 4
 
0.1%
E 2
 
0.1%
L 2
 
0.1%
Other values (5) 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1043
32.9%
U 1042
32.8%
S 1041
32.8%
C 12
 
0.4%
N 10
 
0.3%
Y 9
 
0.3%
A 4
 
0.1%
R 4
 
0.1%
E 2
 
0.1%
L 2
 
0.1%
Other values (5) 5
 
0.2%

Total assets
Real number (ℝ)

HIGH CORRELATION 

Distinct1057
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2530087 × 108
Minimum1759950
Maximum1.52041 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:45.247802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1759950
5-th percentile12029175
Q138214775
median98961500
Q32.5158876 × 108
95-th percentile1.194711 × 109
Maximum1.52041 × 1010
Range1.520234 × 1010
Interquartile range (IQR)2.1337398 × 108

Descriptive statistics

Standard deviation8.8427004 × 108
Coefficient of variation (CV)2.7183144
Kurtosis97.640083
Mean3.2530087 × 108
Median Absolute Deviation (MAD)73181950
Skewness8.2196806
Sum3.4416832 × 1011
Variance7.8193351 × 1017
MonotonicityNot monotonic
2024-08-13T16:52:45.373807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100625000 2
 
0.2%
1250472000 1
 
0.1%
609148032 1
 
0.1%
498161984 1
 
0.1%
48730200 1
 
0.1%
59676800 1
 
0.1%
137048000 1
 
0.1%
161328992 1
 
0.1%
200867008 1
 
0.1%
128797000 1
 
0.1%
Other values (1047) 1047
99.0%
ValueCountFrequency (%)
1759950 1
0.1%
1883860 1
0.1%
2115680 1
0.1%
2751020 1
0.1%
3435810 1
0.1%
3520330 1
0.1%
3528430 1
0.1%
4084620 1
0.1%
4594320 1
0.1%
4606790 1
0.1%
ValueCountFrequency (%)
1.52041001 × 10101
0.1%
8043539968 1
0.1%
7433039872 1
0.1%
7170240000 1
0.1%
6478000000 1
0.1%
6365200000 1
0.1%
5666699776 1
0.1%
5624711000 1
0.1%
5270000000 1
0.1%
4654879744 1
0.1%

EPS Annual
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2003865
Minimum0.0067
Maximum49.3043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:45.504098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0067
5-th percentile0.30814
Q11.355325
median3.1739
Q36.5034
95-th percentile16.587825
Maximum49.3043
Range49.2976
Interquartile range (IQR)5.148075

Descriptive statistics

Standard deviation6.3935496
Coefficient of variation (CV)1.2294374
Kurtosis13.074879
Mean5.2003865
Median Absolute Deviation (MAD)2.17455
Skewness3.1271343
Sum5502.0089
Variance40.877477
MonotonicityNot monotonic
2024-08-13T16:52:45.626649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1969 2
 
0.2%
4.6302 2
 
0.2%
0.2414 2
 
0.2%
0.8564 2
 
0.2%
1.5719 2
 
0.2%
1.5482 1
 
0.1%
11.3591 1
 
0.1%
2.0542 1
 
0.1%
5.1507 1
 
0.1%
0.9938 1
 
0.1%
Other values (1043) 1043
98.6%
ValueCountFrequency (%)
0.0067 1
0.1%
0.0086 1
0.1%
0.0453 1
0.1%
0.0496 1
0.1%
0.0541 1
0.1%
0.0591 1
0.1%
0.0777 1
0.1%
0.0908 1
0.1%
0.1043 1
0.1%
0.1052 1
0.1%
ValueCountFrequency (%)
49.3043 1
0.1%
44.7325 1
0.1%
44.1144 1
0.1%
44.11 1
0.1%
43.9124 1
0.1%
42.0619 1
0.1%
40.7296 1
0.1%
40.5785 1
0.1%
39.2103 1
0.1%
36.5096 1
0.1%

Dividend Per Share Annual
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1017
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9810201
Minimum0
Maximum26.5818
Zeros14
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:45.742166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.139185
Q10.60275
median1.2186
Q32.5614
95-th percentile6.01439
Maximum26.5818
Range26.5818
Interquartile range (IQR)1.95865

Descriptive statistics

Standard deviation2.3418225
Coefficient of variation (CV)1.1821296
Kurtosis24.428173
Mean1.9810201
Median Absolute Deviation (MAD)0.7774
Skewness3.8378567
Sum2095.9193
Variance5.4841325
MonotonicityNot monotonic
2024-08-13T16:52:45.868075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
1.3%
0.2008 3
 
0.3%
0.2 3
 
0.3%
0.6639 2
 
0.2%
0.1202 2
 
0.2%
1.8416 2
 
0.2%
1.1397 2
 
0.2%
1.1189 2
 
0.2%
0.8521 2
 
0.2%
2.2977 2
 
0.2%
Other values (1007) 1024
96.8%
ValueCountFrequency (%)
0 14
1.3%
0.0004 1
 
0.1%
0.0061 1
 
0.1%
0.01 1
 
0.1%
0.016 1
 
0.1%
0.0201 1
 
0.1%
0.0374 1
 
0.1%
0.04 1
 
0.1%
0.041 2
 
0.2%
0.0411 1
 
0.1%
ValueCountFrequency (%)
26.5818 1
0.1%
20.335 1
0.1%
20.2984 1
0.1%
18.089 1
0.1%
17.4587 1
0.1%
14.6594 1
0.1%
13.1416 1
0.1%
12.5526 1
0.1%
12.3475 1
0.1%
12.092 1
0.1%

EBITDA CAGR (5y)
Real number (ℝ)

Distinct942
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6625037
Minimum-55
Maximum123.28
Zeros0
Zeros (%)0.0%
Negative229
Negative (%)21.6%
Memory size8.4 KiB
2024-08-13T16:52:45.987377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-55
5-th percentile-11.2945
Q10.92
median7.21
Q314.27
95-th percentile33.3275
Maximum123.28
Range178.28
Interquartile range (IQR)13.35

Descriptive statistics

Standard deviation15.121723
Coefficient of variation (CV)1.7456527
Kurtosis9.9057822
Mean8.6625037
Median Absolute Deviation (MAD)6.61
Skewness1.7014433
Sum9164.9289
Variance228.66651
MonotonicityNot monotonic
2024-08-13T16:52:46.111783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.62 3
 
0.3%
-3.64 3
 
0.3%
10.2 3
 
0.3%
3.13 3
 
0.3%
9 3
 
0.3%
7.37 3
 
0.3%
6.34 3
 
0.3%
8.41 3
 
0.3%
8.44 3
 
0.3%
4.45 3
 
0.3%
Other values (932) 1028
97.2%
ValueCountFrequency (%)
-55 1
0.1%
-51.79 1
0.1%
-36.74 1
0.1%
-32.95 1
0.1%
-31.16 1
0.1%
-28.58 1
0.1%
-27.27 1
0.1%
-26.12 1
0.1%
-24.54 1
0.1%
-23.28 1
0.1%
ValueCountFrequency (%)
123.28 1
0.1%
120.5 1
0.1%
97.73 1
0.1%
90.98 1
0.1%
89.72 1
0.1%
81.43 1
0.1%
74.49 1
0.1%
66.64 1
0.1%
65.03 1
0.1%
59.5 1
0.1%

EBITDA
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8788665 × 109
Minimum-1.153977 × 109
Maximum1.8322199 × 1011
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)0.7%
Memory size8.4 KiB
2024-08-13T16:52:46.235179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.153977 × 109
5-th percentile32149250
Q12.28685 × 108
median6.944305 × 108
Q32.1318455 × 109
95-th percentile1.140125 × 1010
Maximum1.8322199 × 1011
Range1.8437597 × 1011
Interquartile range (IQR)1.9031605 × 109

Descriptive statistics

Standard deviation9.5709204 × 109
Coefficient of variation (CV)3.3245447
Kurtosis182.18581
Mean2.8788665 × 109
Median Absolute Deviation (MAD)5.930285 × 108
Skewness11.853432
Sum3.0458407 × 1012
Variance9.1602517 × 1019
MonotonicityNot monotonic
2024-08-13T16:52:46.355220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
756800000 2
 
0.2%
728000000 2
 
0.2%
1871000064 2
 
0.2%
1318000000 2
 
0.2%
5130999808 2
 
0.2%
322076992 1
 
0.1%
4445000192 1
 
0.1%
396782016 1
 
0.1%
302948000 1
 
0.1%
244400992 1
 
0.1%
Other values (1043) 1043
98.6%
ValueCountFrequency (%)
-1153976960 1
0.1%
-840955008 1
0.1%
-29463000 1
0.1%
-14743000 1
0.1%
-8056000 1
0.1%
-3232000 1
0.1%
-995000 1
0.1%
720000 1
0.1%
1108000 1
0.1%
1572801 1
0.1%
ValueCountFrequency (%)
1.832219935 × 10111
0.1%
1.317810012 × 10111
0.1%
1.29433002 × 10111
0.1%
7.090299699 × 10101
0.1%
4.927499878 × 10101
0.1%
4.81110016 × 10101
0.1%
4.210099814 × 10101
0.1%
4.095499878 × 10101
0.1%
3.974900122 × 10101
0.1%
3.751399834 × 10101
0.1%

ROI Annual
Real number (ℝ)

HIGH CORRELATION 

Distinct853
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.225898
Minimum-5.09
Maximum124.92
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.2%
Memory size8.4 KiB
2024-08-13T16:52:46.604681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5.09
5-th percentile1.158
Q14.3825
median8.49
Q314.685
95-th percentile28.573
Maximum124.92
Range130.01
Interquartile range (IQR)10.3025

Descriptive statistics

Standard deviation10.395069
Coefficient of variation (CV)0.92598999
Kurtosis18.47408
Mean11.225898
Median Absolute Deviation (MAD)4.69
Skewness2.9976721
Sum11877
Variance108.05746
MonotonicityNot monotonic
2024-08-13T16:52:46.716165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.67 4
 
0.4%
9.29 4
 
0.4%
1.17 4
 
0.4%
5.37 4
 
0.4%
12.97 3
 
0.3%
3.7 3
 
0.3%
7.18 3
 
0.3%
8.62 3
 
0.3%
4.89 3
 
0.3%
2.06 3
 
0.3%
Other values (843) 1024
96.8%
ValueCountFrequency (%)
-5.09 1
0.1%
-0.9 1
0.1%
0.02 1
0.1%
0.14 1
0.1%
0.21 1
0.1%
0.22 1
0.1%
0.26 1
0.1%
0.27 1
0.1%
0.29 1
0.1%
0.32 2
0.2%
ValueCountFrequency (%)
124.92 1
0.1%
77.22 1
0.1%
70.97 1
0.1%
64.17 1
0.1%
61.5 1
0.1%
60.31 1
0.1%
56.48 1
0.1%
55.66 1
0.1%
54.47 1
0.1%
53.22 1
0.1%

Ratio Debt/Equity (Annual)
Real number (ℝ)

ZEROS 

Distinct936
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2406917
Minimum0
Maximum262.3333
Zeros95
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:46.834623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.285075
median0.6718
Q31.342525
95-th percentile6.975925
Maximum262.3333
Range262.3333
Interquartile range (IQR)1.05745

Descriptive statistics

Standard deviation11.163439
Coefficient of variation (CV)4.9821396
Kurtosis345.96366
Mean2.2406917
Median Absolute Deviation (MAD)0.4728
Skewness16.886954
Sum2370.6518
Variance124.62237
MonotonicityNot monotonic
2024-08-13T16:52:46.954461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
 
9.0%
0.3464 3
 
0.3%
0.4869 3
 
0.3%
0.4877 2
 
0.2%
0.7168 2
 
0.2%
0.6694 2
 
0.2%
0.2245 2
 
0.2%
1.0707 2
 
0.2%
1.1643 2
 
0.2%
0.0094 2
 
0.2%
Other values (926) 943
89.1%
ValueCountFrequency (%)
0 95
9.0%
0.0001 1
 
0.1%
0.0004 2
 
0.2%
0.0008 1
 
0.1%
0.001 1
 
0.1%
0.0012 2
 
0.2%
0.0015 1
 
0.1%
0.0018 2
 
0.2%
0.0021 2
 
0.2%
0.0034 1
 
0.1%
ValueCountFrequency (%)
262.3333 1
0.1%
181.8148 1
0.1%
80.9825 1
0.1%
56.3822 1
0.1%
54.2028 1
0.1%
51.7982 1
0.1%
51.3333 1
0.1%
46.3863 1
0.1%
44.7062 1
0.1%
44.048 1
0.1%

Dividend Yield Indicated Annual
Real number (ℝ)

ZEROS 

Distinct1041
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8120763
Minimum0
Maximum33.41772
Zeros16
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2024-08-13T16:52:47.075789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.338906
Q11.1669291
median2.1742185
Q33.8338941
95-th percentile7.2242285
Maximum33.41772
Range33.41772
Interquartile range (IQR)2.666965

Descriptive statistics

Standard deviation2.4841457
Coefficient of variation (CV)0.8833849
Kurtosis23.792507
Mean2.8120763
Median Absolute Deviation (MAD)1.2245261
Skewness3.0624557
Sum2975.1767
Variance6.1709801
MonotonicityNot monotonic
2024-08-13T16:52:47.187635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
1.5%
3.508772 2
 
0.2%
3.937008 2
 
0.2%
1.4912391 1
 
0.1%
0.81103 1
 
0.1%
2.3157349 1
 
0.1%
1.4851485 1
 
0.1%
2.1427913 1
 
0.1%
0.40202498 1
 
0.1%
4.175279 1
 
0.1%
Other values (1031) 1031
97.4%
ValueCountFrequency (%)
0 16
1.5%
0.01611344 1
 
0.1%
0.02446483 1
 
0.1%
0.02486 1
 
0.1%
0.0404408 1
 
0.1%
0.08841733 1
 
0.1%
0.09412973 1
 
0.1%
0.09436188 1
 
0.1%
0.0996016 1
 
0.1%
0.12830108 1
 
0.1%
ValueCountFrequency (%)
33.41772 1
0.1%
14.757281 1
0.1%
14.375507 1
0.1%
13.649538 1
0.1%
13.56185 1
0.1%
12.834224 1
0.1%
12.285714 1
0.1%
12.280702 1
0.1%
12.048193 1
0.1%
11.782033 1
0.1%

Interactions

2024-08-13T16:52:36.736258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.247904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.997043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.910960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.823626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.565182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.868796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.066285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.061370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.209873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.258749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.139234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.184414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.236268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.214428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.263133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:28.303472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.363215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.515201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:34.703218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.841833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.341326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.090230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.995833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.913097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.653966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.972711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.159842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.160027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.304738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.351510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.236138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.278257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.334287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.310744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.358040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:28.400906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.466446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.615215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:34.802273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.947554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.430474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.183681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.090276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.002648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.744459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.078777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.262416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.262194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.402213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.452166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.339868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.375680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.435595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.411385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.459944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:28.502664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.566393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.714340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:34.902176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.050198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.517549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.279968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.176391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.089137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.835395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.181021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.360415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.364998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.498154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.544699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.436255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.469415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.535404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.503006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.552799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:28.598718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.670043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.814961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.000181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.141887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.600079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.367051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.262173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.166137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.950946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.272608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.453430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.457635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.585919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.630476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.526241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.554915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.629237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.593081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.641845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:28.691972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.762766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.901149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.094041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.236665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.681884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.452692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.344833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.244537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:06.082929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.369359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.546771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.674600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.679921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.722225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.618816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.645118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.720632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.683953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.731014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:28.781782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.863616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.996774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.182764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.349668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.778639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.552636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.438632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.332898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:06.215563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.472945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.652738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.775265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.778690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.819199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.839124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.782674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.822987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.788030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.837894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:28.928508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.972239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.103340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.292969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.560219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.865767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.742855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.526222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.424074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:06.456318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.576977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.752007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.877819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.880296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.917401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.932730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.880787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.924292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.997576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.937511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.037435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.193118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.207782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.392052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.661354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:58.956621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.834205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.619091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.516013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:06.563440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.734485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.856189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:12.992919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.981253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.017376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.038386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.990024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.027661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.105057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.049108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.145137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.300899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.362489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.496656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.756752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.040802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:00.920758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.704252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.601744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:06.679910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.831758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:10.952741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.098144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.084593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.110943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.131801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.080558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.130082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.205520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.141062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.244946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.403665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.482895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.595060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.851698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.119859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.002791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.785677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.681497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:06.786720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:08.927818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.048331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.196316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.177610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.196782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.221045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.172126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.222466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.292042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.234311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.339289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.499530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.578367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.705222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:37.952126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.206353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.089637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.871203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.771279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:06.901903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.026082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.143866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.294682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.273525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.287231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.312428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.263830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.321391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.386393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.326539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.441489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.599573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.676564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.805128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:38.047416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.287012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.172377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:02.949414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.860128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.006475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.120919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.232970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.386569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.359150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.372254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.399462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.352402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.412145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.480169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.416083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.529955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.695991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.763493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:35.897985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:38.154515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.377678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.265842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.043301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:04.953169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.122862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.232057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.334055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.489045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.573971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.472834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.500782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.454856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.512232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.578582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.514453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.676213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.798012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.868906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.008357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:38.250803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.461784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.354330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.138981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.040142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.223209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.333474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.436512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.592208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.661239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.560234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.593211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.547367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.611261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.673904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.604297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.769921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.894217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:33.964998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.106100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:38.353704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.545373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.442255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.234610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.123937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.328617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.431443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.531438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.694024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.752102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.655494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.685434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.636545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.706427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.761577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.699667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.866045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:31.998491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:34.063927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.206381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:38.455454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.634751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.537699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.443583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.209936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.435613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.656596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.637258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.795482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.855714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.752698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.783206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.734594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.806985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.864669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.798482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:29.960037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.105518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:34.172087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.341711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:38.571335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.728356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.633061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.540296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.300939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.552171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.758951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.745269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:13.907259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:15.955787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.851228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.883868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:21.832259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:23.909125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:25.961865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.899125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.065683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.216748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:34.406916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.448568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:38.676815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.817189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.724150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.631751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.391586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.661121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.856346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.850889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.008395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.059075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:17.945053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:19.980302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.042524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.006327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.058971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:27.996575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.165016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.312929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:34.502565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.538985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:38.778612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:51:59.901964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:01.815580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:03.724847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:05.478662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:07.767234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:09.960014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:11.952718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:14.107112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:16.154322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:18.040975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:20.077631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:22.136550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:24.108073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:26.163528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:28.206714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:30.260774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:32.412498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:34.599631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T16:52:36.632976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-13T16:52:47.312774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
52 Weeks High52 Weeks LowBetaChiffre d'affairesCountryCurrencyDividend Per Share AnnualDividend Yield Indicated AnnualEBITDAEBITDA CAGR (5y)EPS AnnualExchangeMarket Cap (in M)P/E RatioPerformance (52 weeks)PricePrice 52 Weeks AgoROI AnnualRatio Debt/Equity (Annual)Résultat netSectorTotal assetsVolume 1 monthVolume 52 weeks
52 Weeks High1.0000.9800.1530.4600.0000.0000.577-0.4720.4660.2030.7890.0000.6210.2060.1590.9860.9820.3450.0410.5460.1010.0790.0790.068
52 Weeks Low0.9801.0000.0970.4730.0000.0000.610-0.4260.4960.1860.7810.0000.6500.2100.1150.9840.9860.3270.0470.5750.1030.1180.0940.086
Beta0.1530.0971.0000.0100.1360.134-0.077-0.2280.0030.0440.0240.1200.0620.1850.0820.1330.111-0.0100.0460.0010.167-0.0180.0390.012
Chiffre d'affaires0.4600.4730.0101.0000.0000.1090.341-0.1280.8960.0030.4600.0300.808-0.0540.0450.4600.4640.1380.2620.8030.0990.6870.6790.686
Country0.0000.0000.1360.0001.0000.7750.2090.2090.0190.1880.0700.1790.0000.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.000
Currency0.0000.0000.1340.1090.7751.0000.2570.1590.1250.0000.1460.0290.0000.0000.0000.0000.0000.0000.0000.0970.0760.0000.0000.000
Dividend Per Share Annual0.5770.610-0.0770.3410.2090.2571.0000.2660.4190.0680.5630.0680.4400.0230.0420.5820.5970.1440.1890.4240.0130.1400.1050.121
Dividend Yield Indicated Annual-0.472-0.426-0.228-0.1280.2090.1590.2661.000-0.056-0.187-0.2890.063-0.212-0.227-0.222-0.470-0.426-0.2640.194-0.1560.2000.0640.0590.086
EBITDA0.4660.4960.0030.8960.0190.1250.419-0.0561.0000.0920.4680.0460.907-0.0080.1050.4860.4720.1210.3320.9000.1170.7970.7570.769
EBITDA CAGR (5y)0.2030.1860.0440.0030.1880.0000.068-0.1870.0921.0000.2890.0840.102-0.0950.1920.2140.1700.2250.0090.1500.048-0.028-0.056-0.055
EPS Annual0.7890.7810.0240.4600.0700.1460.563-0.2890.4680.2891.0000.0850.458-0.3490.1060.7730.7780.5610.0160.5910.0830.0300.0400.041
Exchange0.0000.0000.1200.0300.1790.0290.0680.0630.0460.0840.0851.0000.0720.0170.1320.0740.0040.1020.0000.0710.3020.1030.0000.044
Market Cap (in M)0.6210.6500.0620.8080.0000.0000.440-0.2120.9070.1020.4580.0721.0000.2300.1850.6510.6220.1750.2420.8710.0790.7780.7280.731
P/E Ratio0.2060.2100.185-0.0540.0000.0000.023-0.227-0.008-0.095-0.3490.0170.2301.0000.1910.2420.199-0.3860.071-0.0880.0720.0990.0440.024
Performance (52 weeks)0.1590.1150.0820.0450.0000.0000.042-0.2220.1050.1920.1060.1320.1850.1911.0000.2420.0210.116-0.0050.1850.0730.040-0.012-0.006
Price0.9860.9840.1330.4600.0000.0000.582-0.4700.4860.2140.7730.0740.6510.2420.2421.0000.9660.3330.0470.5710.1000.1080.0850.078
Price 52 Weeks Ago0.9820.9860.1110.4640.0000.0000.597-0.4260.4720.1700.7780.0040.6220.1990.0210.9661.0000.3160.0520.5400.1120.0960.0900.082
ROI Annual0.3450.327-0.0100.1380.0000.0000.144-0.2640.1210.2250.5610.1020.175-0.3860.1160.3330.3161.000-0.2840.3290.159-0.0090.003-0.000
Ratio Debt/Equity (Annual)0.0410.0470.0460.2620.0000.0000.1890.1940.3320.0090.0160.0000.2420.071-0.0050.0470.052-0.2841.0000.1830.0560.2790.2880.287
Résultat net0.5460.5750.0010.8030.0000.0970.424-0.1560.9000.1500.5910.0710.871-0.0880.1850.5710.5400.3290.1831.0000.0500.6860.6410.651
Sector0.1010.1030.1670.0990.1120.0760.0130.2000.1170.0480.0830.3020.0790.0720.0730.1000.1120.1590.0560.0501.0000.1230.0410.044
Total assets0.0790.118-0.0180.6870.0000.0000.1400.0640.797-0.0280.0300.1030.7780.0990.0400.1080.096-0.0090.2790.6860.1231.0000.8950.903
Volume 1 month0.0790.0940.0390.6790.0000.0000.1050.0590.757-0.0560.0400.0000.7280.044-0.0120.0850.0900.0030.2880.6410.0410.8951.0000.986
Volume 52 weeks0.0680.0860.0120.6860.0000.0000.1210.0860.769-0.0550.0410.0440.7310.024-0.0060.0780.082-0.0000.2870.6510.0440.9030.9861.000

Missing values

2024-08-13T16:52:38.952934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-13T16:52:39.290654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SymbolCompany NamePriceMarket Cap (in M)P/E RatioBetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryChiffre d'affairesRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualDividend Per Share AnnualEBITDA CAGR (5y)EBITDAROI AnnualRatio Debt/Equity (Annual)Dividend Yield Indicated Annual
0COLMColumbia Sportswear Co81.83504781.35748718.83610.628373461903.9130435.568954e+0587.2366.010NASDAQ0.098148US3.385903e+09227407008.0Consumer CyclicalApparel Manufacturing74.540024USD1.250472e+094.09291.20301.65421724992.012.970.00001.491239
1HCKTHackett Group Inc25.5550711.35649220.49760.40413797656.1422921.270293e+0527.6820.230NASDAQ0.090217US2.974240e+0834749000.0TechnologyInformation Technology Services23.445827USD5.962300e+071.23570.44207.9359527000.027.810.36311.707412
2LFUSLittelfuse Inc241.95005987.85872122.38721.125715118391.7391301.003712e+05275.45212.800NASDAQ-0.060917US2.234752e+09194587008.0TechnologyElectronic Components257.600555USD5.715050e+0810.33722.50089.88427772000.07.740.35191.197093
3BRKRBruker Corp63.36009508.43683321.36131.482279942371.8379451.164853e+0694.7553.810NASDAQ-0.042440US3.119700e+09352100000.0HealthcareMedical Devices66.160316USD1.431900e+092.90220.201316.52551400000.016.070.93050.331675
4VALUValue Line Inc42.8099400.86577421.39740.4677463946.1027671.080267e+0460.2732.070NASDAQ-0.215407US3.748700e+0719016000.0Financial ServicesFinancial Data & Stock Exchanges54.526936USD1.667300e+072.01691.122212.5810488000.020.940.00002.857823
5ARTNAArtesian Resources Corp37.6400384.25563422.18460.58405849199.5138344.061154e+0447.8833.340NASDAQ-0.154395US1.030750e+0818287000.0UtilitiesUtilities - Regulated Water44.492043USD1.664800e+071.66621.13644.9244989000.04.060.78373.286986
6SGASaga Communications Inc16.0000101.02599810.21900.14224118553.1349211.286913e+0426.9614.215NASDAQ-0.118364US1.121330e+086918000.0Communication ServicesBroadcasting18.141815USD4.358700e+071.57153.2145-8.8614193000.05.570.00006.451613
7CRCTCricut Inc6.50001385.23139723.63670.171996654955.5317466.463606e+0511.424.430NASDAQ-0.336847US7.414940e+0867929000.0TechnologyComputer Hardware9.790630USD9.280990e+080.24411.344217.20133451000.010.030.00001.584786
8CHDNChurchill Downs Inc134.86009929.59192323.29871.191327408702.6666674.644292e+05146.54106.820NASDAQ0.095796US2.615300e+09407500000.0Consumer CyclicalGambling123.101227USD2.417270e+085.48360.363022.59856300032.07.235.45850.288454
9PAHCPhibro Animal Health Corp17.4600713.67357321.74270.902656149978.4841271.227599e+0519.559.400NASDAQ0.191893US9.995640e+0813162000.0HealthcareDrug Manufacturers - Specialty & Generic14.656014USD2.992890e+080.80490.4800-3.2493794000.04.301.68072.823529
SymbolCompany NamePriceMarket Cap (in M)P/E RatioBetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryChiffre d'affairesRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualDividend Per Share AnnualEBITDA CAGR (5y)EBITDAROI AnnualRatio Debt/Equity (Annual)Dividend Yield Indicated Annual
1048EQREquity Residential71.3526687.37325431.94420.6391961.959911e+062.162622e+0672.240052.58NYSE0.120911US2.916548e+099.535790e+08Real EstateREIT - Residential63.673508USD379136000.02.13722.62161.401.799678e+094.510.67283.835772
1049CRSCarpenter Technology Corp139.316910.927797122.53420.2898495.663270e+058.294783e+05148.940056.42NYSE1.457937US2.759700e+091.865000e+08IndustrialsMetal Fabrication56.817425USD49608300.01.14630.8130-3.524.887000e+082.700.49640.572205
1050PSAPublic Storage317.0454810.52011425.51311.0703037.800187e+057.527391e+05318.5025233.51NYSE0.154123US4.659026e+091.880469e+09Real EstateREIT - Industrial274.809937USD175018000.012.196513.14169.523.349156e+0911.240.90913.831784
1051PRIPrimerica Inc254.418754.54591815.18300.8633141.542476e+051.304304e+05256.5600184.76NYSE0.208879US3.047349e+094.412330e+08Financial ServicesInsurance - Life210.560547USD33827500.016.00472.609711.839.554510e+0814.250.95881.443985
1052METMetLife Inc70.1349113.78562231.12410.6352543.460582e+063.333617e+0679.290057.91NYSE0.138521US6.877000e+102.708000e+09Financial ServicesInsurance - Life61.619362USD700324992.02.07012.3367-13.645.244000e+093.230.62733.129935
1053ALEALLETE Inc64.103702.04998214.98200.1803493.892996e+054.397478e+0565.860049.29NYSE0.168283US1.539300e+092.211000e+08UtilitiesUtilities - Diversified54.890205USD57754300.04.30492.70914.124.284000e+085.370.63834.394577
1054WSOWatsco Inc471.3118999.63195435.42481.5597223.066147e+052.941522e+05520.3500338.58NYSE0.350070US7.434361e+094.854870e+08IndustrialsIndustrial Distribution349.387604USD34789300.014.68148.707315.877.683850e+0823.710.01452.292410
1055SGUStar Group LP11.04382.66234911.9788-0.2260774.498492e+043.037826e+0414.76009.64NYSE-0.008014US1.792705e+094.405500e+07EnergyOil & Gas Refining & Marketing11.128944USD34661400.00.89500.6564-0.931.149180e+087.760.56146.233063
1056NSCNorfolk Southern Corp239.6254177.22761229.65370.8009471.256250e+061.239665e+06263.6600183.09NYSE0.137450US1.209200e+101.791000e+09IndustrialsRailroads210.738556USD226096000.08.03435.4003-3.575.578000e+096.101.34412.231129
1057CPACopa Holdings SA88.123673.4571447.14551.1823493.297429e+053.127565e+05114.000078.12NYSE-0.051162PA3.493420e+096.713850e+08IndustrialsAirlines92.858147USD30748900.012.77963.294520.571.040345e+0913.290.82297.360841